An enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm
Genetic modifications, such as gene knockout technique, have become mainstream in metabolic engineering to produce desired amount of targeted metabolites through reconstruction of the metabolic networks. The production, however, does not often achieve desirable outcome. To this end, in-silico method...
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| Format: | Book Section |
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Springer Science and Business Media Deutschland GmbH
2022
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| _version_ | 1796866970635730944 |
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| author | Siow, Shen Yee Mohamad, Mohd. Saberi Choon, Yee Wen Remli, Muhammad Akmal Abdul Majid, Hairudin |
| author_facet | Siow, Shen Yee Mohamad, Mohd. Saberi Choon, Yee Wen Remli, Muhammad Akmal Abdul Majid, Hairudin |
| author_sort | Siow, Shen Yee |
| collection | ePrints |
| description | Genetic modifications, such as gene knockout technique, have become mainstream in metabolic engineering to produce desired amount of targeted metabolites through reconstruction of the metabolic networks. The production, however, does not often achieve desirable outcome. To this end, in-silico methods have been applied to predict potential metabolic network response and optimise production. Previous methods working on relational modelling framework, such as OptKnock and OptGene, however, failed at handling its multivariable and multimodal functions optimization algorithms. This paper proposes hybridising bacterial foraging optimizationg algorithm (BFO) and dynamic flux balance analysis (DFBA) to overcome problems in OptKnock and OptGene with a nature-inspired algorithm and also to couple kinematic variables in the model to predict production of succinate in E.coli model. In-silico results showed that by knocking out genes identifed by BFODFBA, production rate of succinate is better as when compared to OptKnock and OptGene. |
| first_indexed | 2024-03-05T21:20:10Z |
| format | Book Section |
| id | utm.eprints-100937 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T21:20:10Z |
| publishDate | 2022 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| record_format | dspace |
| spelling | utm.eprints-1009372023-05-27T07:39:21Z http://eprints.utm.my/100937/ An enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm Siow, Shen Yee Mohamad, Mohd. Saberi Choon, Yee Wen Remli, Muhammad Akmal Abdul Majid, Hairudin QA75 Electronic computers. Computer science Genetic modifications, such as gene knockout technique, have become mainstream in metabolic engineering to produce desired amount of targeted metabolites through reconstruction of the metabolic networks. The production, however, does not often achieve desirable outcome. To this end, in-silico methods have been applied to predict potential metabolic network response and optimise production. Previous methods working on relational modelling framework, such as OptKnock and OptGene, however, failed at handling its multivariable and multimodal functions optimization algorithms. This paper proposes hybridising bacterial foraging optimizationg algorithm (BFO) and dynamic flux balance analysis (DFBA) to overcome problems in OptKnock and OptGene with a nature-inspired algorithm and also to couple kinematic variables in the model to predict production of succinate in E.coli model. In-silico results showed that by knocking out genes identifed by BFODFBA, production rate of succinate is better as when compared to OptKnock and OptGene. Springer Science and Business Media Deutschland GmbH 2022 Book Section PeerReviewed Siow, Shen Yee and Mohamad, Mohd. Saberi and Choon, Yee Wen and Remli, Muhammad Akmal and Abdul Majid, Hairudin (2022) An enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm. In: Proceedings of International Conference on Emerging Technologies and Intelligent Systems ICETIS 2021 Volume 2. Lecture Notes in Networks and Systems, 322 (2). Springer Science and Business Media Deutschland GmbH, Cham, Switzerland, pp. 591-601. ISBN 978-303085989-3 http://dx.doi.org/10.1007/978-3-030-85990-9_47 DOI:10.1007/978-3-030-85990-9_47 |
| spellingShingle | QA75 Electronic computers. Computer science Siow, Shen Yee Mohamad, Mohd. Saberi Choon, Yee Wen Remli, Muhammad Akmal Abdul Majid, Hairudin An enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm |
| title | An enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm |
| title_full | An enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm |
| title_fullStr | An enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm |
| title_full_unstemmed | An enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm |
| title_short | An enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm |
| title_sort | enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm |
| topic | QA75 Electronic computers. Computer science |
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